Contextual information embedding [1]}, [2]} is an indispensable process. It further refines the extracted contextual information, which can fuse the contextual information of all channels on the same receptive field and map more abstract information. Specifically, to save parameters and computational complexity, we perform a shared two-layer MLP to generate the embedding of the local and global contextual information. The first layer is used for dimension reduction and maps \(\mathbb {R}^{C}\)  \(\mapsto \)  \(\mathbb {R}^{\widehat{C}}\) . The second layer is used to increase the dimension to be consistent with the input and maps \(\mathbb {R}^{\widehat{C}}\)  \(\mapsto \)  \(\mathbb {R}^{C}\) . Meanwhile, to improve the robustness, each layer is followed by an activation function, i.e., ReLu [3]}. The following formula \(f(\bullet )\)  expresses such a process:
\(f(X) = \sigma (W_2(\sigma (W_1X))),\) 
